Complete Bootcamp 2021 : Feature selection using Python

MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + srt | Duration: 40 lectures (3h 41m) | Size: 1.26 GB

Filter methods selector like variance, F-Score, Mutual Information etc.

A Course by Kaggle grandmaster on Feature Selection : Machine Learning, Scikit Learn, Pandas, mlextend, clean your data

Feature Selection using Python machine learning packages Pandas, scikit-learn(sklearn), mlxtend

Learn the concept behind feature selection, detail discussion on feature selection method (filter, wrapper and embedded)

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Wrapper Method : Exhaustive, Forward and Backward Selection

Embedded Method : Lasso Decision Tree, Random Forest, ExtraTree etc

Implemented with more than 15 Projects

Ready to use code in machine learning projects

Feature selection technique people used in Competitions.

Familiarity with Python programming

Working knowledge of Jupyter Notebook

Working Knowledge of Pandas and Numpy

Working Knowledge of Machine learning Model Creation using sklearn

Understanding of Statistical methods like chisquare test

Feature selection is one of most important activity in machine learning/Artificial Intelligence pipeline. We select all relevant features for machine learning algorithm and discard less relevant or not relevant features. Feature selection is also known as variable selection.This course will provide learner, detailed knowledge of feature selection. It is one of most detailed online course on feature selection.

Who is this course for

Data scientist who wants to create faster and more interpretable machine learning models.

Data analyst who wants to relation between two variables.

Data science aspirants who are preparing for data science interview.

Any One who wants to learn about feature selection process.

AI/ML software eeer who write code for machine learning.

Teachers who are teaching Machine Learning Models.

What will you learn

In this course, you are going to learn feature selection by doing. I have included more than 8 end to end small projects on feature selection methods. Each method has one project so that learner can understand the process fully. Code provided in throughout course is able. You can code and data and run by yourself to get confidence. Knowledge gain though this course is precious and can be used in We are going to learn following topics.

What is feature selection

Different methods of feature selection.

Filter methods

Minimum variance method

F-Score using correlation for regression analysis data.

Anova F for classification analysis data

Mutual Information for regression and Classification analysis data.

Chi-Square Scores for categorical features and Target

All these methods implementation using sklearn

Wrapper Method

Forward selection of features.

Backward selection of features.

Exhaustive feature selection.

Implementation of each using sklearn and mlxtend.

Embedded Method

Introduction to Embedded Method for feature selection.

Using RandomForest

Using Extremely randomized trees to select features

Regularization based feature selection

So what are you waiting for Join the course and get the knowledge of variable selection and apply it in your projects to get efficient and interpretable machine learning models.

For feature selection this course is for every one

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